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Music Genre Classification Based on VMD-IWOA-XGBOOST

Author

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  • Rumeijiang Gan

    (School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243002, China
    Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan 243032, China)

  • Tichen Huang

    (Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan 243032, China
    School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China)

  • Jin Shao

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Fuyu Wang

    (Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan 243032, China
    School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China)

Abstract

Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.

Suggested Citation

  • Rumeijiang Gan & Tichen Huang & Jin Shao & Fuyu Wang, 2024. "Music Genre Classification Based on VMD-IWOA-XGBOOST," Mathematics, MDPI, vol. 12(10), pages 1-24, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1549-:d:1395426
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    References listed on IDEAS

    as
    1. Nag, Sayan & Basu, Medha & Sanyal, Shankha & Banerjee, Archi & Ghosh, Dipak, 2022. "On the application of deep learning and multifractal techniques to classify emotions and instruments using Indian Classical Music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
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